Using images and voice recordings to facilitate underwriting life insurance

ABSTRACT

A system and method for evaluating an insurance applicant as part of an underwriting process to determine one or more appropriate terms of life or other insurance coverage, such as premiums. A processing element employing a neural network is trained to correlate aspects of appearance and/or voice with personal and/or health-related characteristic. A database of images and/or voice recordings of individuals with known personal and/or health-related characteristics is provided for this purpose. The processing element is then provided with an image and/or voice recording of the insurance applicant. The image may be an otherwise non-diagnostic image, such as an ordinary “selfie.” The trained processing element analyzes the image of the insurance applicant, with their permission or affirmative consent, to determine the personal and/or health-related characteristic for the insurance applicant, and then, based upon that analysis, facilitates the underwriting process and/or suggests the one or more appropriate terms of insurance coverage.

RELATED APPLICATIONS

The present patent application is a continuation of, and claims thebenefit of, U.S. patent application Ser. No. 15/266,118, filed Sep. 15,2016 and entitled “Using Images and Voice Recordings to FacilitateUnderwriting Life Insurance,” which is a non-provisional patentapplication which claims priority benefit to U.S. Provisional PatentApplication Ser. No. 62/242,127, entitled “USING IMAGES AND VOICERECORDINGS TO FACILITATE UNDERWRITING LIFE INSURANCE”, filed Oct. 15,2015, which are hereby incorporated by reference in their entireties.Further, the present application is related to identically-titledco-pending U.S. Non-provisional patent application Ser. No. 15/266,033,filed Sep. 15, 2016, which is also hereby incorporated by reference inits entirety.

FIELD OF THE INVENTION

The present disclosure generally relates to systems and methods forevaluating insurance applicants to facilitate underwriting insurancepolicies. More particularly, the present disclosure relates to a systemand a computer-implemented method for analyzing still and/or moving(i.e., video) images, and/or voice recordings of insurance applicants aspart of an underwriting process to determine appropriate insurancepremiums and/or other terms of coverage.

BACKGROUND

The nature of the underwriting process for life insurance includes anumber of factors which limit the ability to sell policies online. Forexample, some providers may require collecting samples of bodily fluidsto assess an applicant's health status. Furthermore, even providers whodo not require such samples are at risk of receiving fraudulent answersto personal and/or health-related questions, such as the applicantfalsely claiming to be a non-smoker. Prior attempts to solve theseproblems include not selling high-benefit policies online, proxying thedesired medical information with advanced statistical techniques usingdata from other sources, and pricing potential fraud into futurepolicies.

Also, machine vision techniques have been employed to extracthealth-related information from images of people. For example, onemachine vision platform is able to diagnose certain medical conditionsbased upon analyses of images from diagnostic imaging tools, such as anX-ray, a CT scan, and/or an MRI scan. The computer's diagnoses may evenbe able to identify ceratin conditions at earlier stages than doctorscould identify them in some situations.

BRIEF SUMMARY

Embodiments of the present technology relate to systems and methods foranalyzing still and/or moving (i.e., video) images and/or voicerecordings of applicants as part of an underwriting process to determineappropriate life insurance premiums and/or other terms of coverage.

In a first aspect, a system for evaluating an insurance applicant aspart of an underwriting process to determine one or more appropriateterms of insurance coverage may broadly comprise a communication elementand a processing element. The communication element may be configured toreceive an image of the insurance applicant. The processing element maybe trained to probablistically correlate an aspect of appearance with apersonal and/or health-related characteristic by being provided with adatabase of images of individuals having known personal and/orhealth-related characteristics. The trained processing element may beconfigured to analyze the image of the insurance applicant toprobablistically determine the personal and/or health-relatedcharacteristic for the insurance applicant, and to suggest theappropriate term of insurance coverage based at least in part on theprobablistically determined personal and/or health-relatedcharacteristic. The system may include more, fewer, or alternativecomponents, including those discussed elsewhere herein.

In another aspect, a computer-implemented method for evaluating aninsurance applicant as part of an underwriting process to determine oneor more appropriate terms of insurance coverage may be provided. Themethod may include training a processing element to probablisticallycorrelate an aspect of appearance with a personal and/or health-relatedcharacteristic by providing the processing element with a database ofimages of individuals having known personal or health-relatedcharacteristics. The method may include receiving with a communicationelement an image of the insurance applicant; analyzing the image of theinsurance applicant with the trained processing element toprobablistically determine the personal and/or health-relatedcharacteristic for the insurance applicant; and/or suggesting with theprocessing element the one or more appropriate terms of insurancecoverage based at least in part on the probablistically determinedpersonal and/or health-related characteristic. The computer-implementedmethod may include more, fewer, or alternative actions, including thosediscussed elsewhere herein.

In another aspect, a non-transitory computer-readable medium with anexecutable program stored thereon for evaluating an insurance applicantas part of an underwriting process to determine one or more appropriateterms of insurance coverage may broadly instruct a system (that includesa communication element and a processing element) to perform thefollowing actions. The processing element may be trained toprobablistically correlate an aspect of appearance with a personaland/or health-related characteristic by providing the processing elementwith a database of images of individuals having known personal orhealth-related characteristics. The communication element may beinstructed to receive an image of the insurance applicant. The trainedprocessing element may be instructed to analyze the image of theinsurance applicant to probablistically determine the personal and/orhealth-related characteristic for the insurance applicant, andinstructed to suggest the one or more appropriate terms of insurancecoverage based at least in part on the probablistically determinedpersonal and/or health-related characteristic. The non-transitorycomputer-readable medium with an executable program stored thereon mayinclude more, fewer, or alternative instructions, including thosediscussed elsewhere herein.

Various implementations of any or all of the foregoing aspects mayinclude any one or more of the following additional features. Theinsurance coverage may be life insurance coverage, and the one or moreappropriate terms of insurance coverage may include an insurancepremium. The image of the insurance applicant may be a digital, analog,still, or moving (i.e., video) image, and the image may be an otherwisenon-diagnostic conventional image, such as a “selfie” taken by theinsurance applicant. The processing element may be trained usingsupervised or unsupervised machine learning, and may employ a neuralnetwork, which may be a convolutional neural network or a deep learningneural network. The personal and/or health-related characteristic maybe, for example, any one more of age, sex, weight, height, ethnicity,lifespan, cause of death, tobacco use, alcohol use, drug use, diet, andexisting medical conditions, and/or risk factors for future medicalconditions.

The communication element may be further configured to receive a voicerecording of the insurance applicant. The processing element may befurther trained to probablistically correlate an aspect of voice withthe personal and/or health-related characteristic by being provided witha database of voice recordings of individuals having the known personaland/or health related characteristics. The processing element may befurther configured to analyze the voice recording of the insuranceapplicant to probablistically determine the personal and/orhealth-related characteristic for the insurance applicant, and tosuggest the appropriate term of insurance coverage based at least inpart on the probablistically determined personal and/or health-relatedcharacteristic.

The processing element may be further configured to use theprobablistically determined personal and/or health-relatedcharacteristic to verify information provided by the insuranceapplicant. The processing element may be further configured to use theprobablistically determined personal and/or health-relatedcharacteristic to wholly or at least partially automatically determinethe one or more appropriate terms of coverage.

Advantages of these and other embodiments will become more apparent tothose skilled in the art from the following description of the exemplaryembodiments which have been shown and described by way of illustration.As will be realized, the present embodiments described herein may becapable of other and different embodiments, and their details arecapable of modification in various respects. Accordingly, the drawingsand description are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of systems andmethods disclosed therein. It should be understood that each Figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals. The present embodiments are notlimited to the precise arrangements and instrumentalities shown in theFigures.

FIG. 1 is a high-level flowchart of an exemplary method embodiment ofthe present technology;

FIG. 2 is a diagram of an exemplary system constructed in accordancewith embodiments of the present technology;

FIG. 3 is a flowchart of an exemplary computer-implemented methodpracticed in accordance with embodiments of the present technology; and

FIG. 4 depicts an exemplary computer-implemented method of providinglife or health insurance quotes based upon, at least in part, video,image, or audio data samples received via wireless communication or datatransmission from an applicant's mobile device.

The Figures depict exemplary embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

The present embodiments may relate to, inter alia, evaluating insuranceapplicants as part of an underwriting process to determine appropriatepremiums and/or other terms of coverage. Broadly characterized, aprocessing element may be trained to probablistically analyze stilland/or moving (i.e., video) images and/or voice recordings of applicantsto determine personal and/or health-related information for an insuranceprovider. More specifically, an applicant desiring life or otherinsurance may provide such one or more still and/or moving (i.e., video)images and/or voice recordings of him- or herself to the insuranceprovider, and the processing element may analyze them to determinepersonal and/or health-related information relevant to an underwritingprocess. The information may be used to determine whether and under whatterms, including appropriate premiums or discounts, the life or otherinsurance should be offered to the applicant.

Machine learning may involve identifying and recognizing patterns inexisting data in order to facilitate making predictions for subsequentdata. Models may be created based upon example inputs in order to makevalid and reliable predictions for novel inputs. In supervised machinelearning, a processing element may be provided with example inputs andtheir associated outputs, and may seek to discover a general rule thatmaps inputs to outputs, so that when subsequent novel inputs areprovided the processing element may, based upon the discovered rule,accurately predict the correct output. In unsupervised machine learning,the processing element may be required to find its own structure inunlabeled example inputs. In one embodiment, machine learning techniquesmay be used to extract the relevant personal and/or health-relatedinformation for insurance applicants from images and/or voice recordingsof those applicants without needing to acquire samples of bodily fluidsor conduct conventional medical reviews.

In one embodiment, a processing element may be trained by providing itwith a large sample of otherwise non-diagnostic conventional analogand/or digital, still and/or moving (i.e., video) images and/or voicerecordings of persons with known personal and/or health-relatedinformation about the persons to analyze for correlations betweendetectable characteristics and the known information. Such informationmay include, for example, age, sex, weight, height, and ethnicity;tobacco, alcohol, and drug use; diet; existing medical conditions andrisk factors for future medical conditions; lifespan and cause of death;and insurance premiums. Based upon these analyses, the processingelement may learn how to identify characteristics and patterns that maythen be applied to analyzing images of new insurance applicants. Forexample, the processing element may learn to determine the applicant'spulse from a video of the applicant, may learn to identify medication orother drug use by the applicant through, e.g., eye movement, and/or maylearn to determine such other information as the applicant's glucoselevel. Similarly, the processing element may learn to identifyindications of certain diseases, disorders, and/or behaviors from avoice recording of the applicant.

Referring to FIG. 1 , once trained, the processing element may receive astill and/or moving (i.e., video) image and/or voice recording of aninsurance applicant, and may probablistically determine the personaland/or health-related characteristic for the insurance applicant, asshown in 10. The resulting data may be used to complete the underwritingprocess, as shown in 12, such as verifying information provided by theapplicant and/or answering underwriting questions, and/or may be used tosubstantially automate the underwriting process by directly predictingthe appropriate insurance premium, as shown in 14. The applicant maythen quickly be provided with a rate quote, as shown in 16.

The large sample of still and/or moving (e.g., video) images and/orvoice recordings used to train the processing element may be, forexample, provided by volunteers, existing policy holders, or taken fromsocial media. The still and/or moving (e.g., video) image and/or voicerecording received from the applicant may be analog or digital andotherwise non-diagnostic and conventional in nature, such as an ordinary“selfie” taken by the insurance applicant or him- or herself. The videosmay include audio of the applicants' voices, and the processingelement's training and analysis may include similarly seeking relevantcharacteristics or patterns in voices. The processing element's analysesof images may be probabilistic, such that the resulting data may beassociated with varying degrees of certainty.

The processing element may employ a neural network, which may be aconvolutional neural network (CNN) and/or a deep learning neuralnetwork. A CNN is a type of feed-forward neural network often used infacial recognition systems, in which individual neurons may be tiled soas to respond to overlapping regions in the visual field. A CNN mayinclude multiple layers of small neuron collections which examine smallportions of an input image, called receptive fields. The results ofthese collections may be tiled so that they overlap to better representthe original image, and this may be repeated for each layer. Deeplearning involves algorithms that attempt to model high-levelabstractions in data by using model architectures, with complexstructures or otherwise, composed of multiple non-lineartransformations. An image may be represented in various ways, such as avector of intensity values per pixel, a set of edges, or regions ofparticular shape. Certain representations may better facilitate learninghow to identify personal and health-related information from examples.

Thus, the present embodiments may be used to probablistically evaluateapplicants for life or other insurance and determine appropriatepremiums or other terms of coverage based upon analyses of still and/ormoving images, and/or voice recordings of the applicants and withoutrequiring conventional medical examinations.

Exemplary System

Referring to FIG. 2 , an exemplary system 20 is shown configured forevaluating an insurance applicant as part of an underwriting process todetermine one or more appropriate terms of life or other insurancecoverage, which may include appropriate premiums. The system 20 maybroadly comprise a memory element 22 configured to store information,such as the database of training images and/or voice recordings; acommunication element 24 configured to receive and transmit signals viaa network 26, including receiving the applicant's image and/or voicerecording; and/or a processing element 28 trained and configured toanalyze the applicant's image and/or voice recording.

More specifically, the memory element 22 may generally allow for storinginformation, such as the database of still and/or moving (e.g., video)images and/or voice recordings used to train the processing element 28,and still and/or moving (e.g., video) images and/or voice recordingsreceived from applicants. The memory element 22 may include data storagecomponents such as read-only memory (ROM), programmable ROM, erasableprogrammable ROM, random-access memory (RAM) such as static RAM (SRAM)or dynamic RAM (DRAM), cache memory, hard disks, floppy disks, opticaldisks, flash memory, thumb drives, USB ports, or the like, orcombinations thereof. The memory element 22 may include, or mayconstitute, a “computer-readable medium.” The memory element 22 mayfurther store instructions, code, code segments, software, firmware,programs, applications, apps, services, daemons, or the like that areexecuted by the processing element 28. The memory element 22 may alsostore additional settings, data, documents, sound files, photographs,movies, images, databases, and the like. The memory element 22 may beelectronically coupled or otherwise in electronic communication with thecommunication element 24 and the processing element 28.

The communication element 24 may generally allow for communication withremote systems or devices, including a system or device 32, such as asmartphone or other mobile communication device, configured to capturethe still and/or moving (e.g., video) image and/or voice recording ofthe applicant. The communication element 24 may include signal or datatransmitting and receiving circuits, such as antennas, amplifiers,filters, mixers, oscillators, digital signal processors (DSPs), and thelike. The communication element 24 may establish communicationwirelessly by utilizing radio-frequency (RF) signals and/or data thatcomply with communication standards such as cellular 2G, 3G, or 4G, IEEE802.11 standard (such as WiFi), IEEE 802.16 standard (such as WiMAX),Bluetooth™, or combinations thereof. The communication element 24 may beelectronically coupled or otherwise in electronic communication with thememory element 22 and the processing element 28.

The network 26 may be embodied by a local, metro, or wide area network(LAN, MAN, or WAN) and may be formed using a plurality of knownarchitectures and topologies. In some embodiments, a portion of thenetwork 26 may be formed by at least a portion of the Internet, bycommunication lines that are leased from other entities, or bycombinations thereof. The network 26 may be implemented within a smallarea such as city or across a larger area such as a region or country.

The processing element 28 may be trained to probablistically correlateone or more aspects of appearance and/or voice with one or more personalor health-related characteristics by being provided with the database ofstill and/or moving (e.g., video) images and/or voice recordings storedin the memory element 22 of individuals having known personal orhealth-related characteristics. The processing element 28 may beconfigured to analyze the still and/or moving image and/or voicerecording of the insurance applicant received via the communicationelement 24 to probablistically determine the personal or health-relatedcharacteristic for the insurance applicant to facilitate the completionof the underwriting process and/or to suggest one or more appropriateterms of insurance coverage, such as an appropriate premium, based atleast in part on the probablistically determined personal orhealth-related characteristic. The processing element 28 may be trainedusing supervised or unsupervised machine learning. Further, theprocessing element 28 may employ a neural network 34, which may be a CNNor a deep learning neural network.

The processing element 28 may include one or more processors,microprocessors, microcontrollers, DSPs, field-programmable gate arrays(FPGAs), analog and/or digital application-specific integrated circuits(ASICs), or the like, or combinations thereof. The processing element 28may generally execute, process, or run instructions, code, codesegments, software, firmware, programs, applications, apps, processes,services, daemons, or the like. The processing element 28 may alsoinclude hardware components such as finite-state machines, sequentialand combinational logic, and other electronic circuits that may performthe functions necessary for the operation of embodiments of the currentinventive concept. The processing element 28 may be in electroniccommunication with the memory element 22 and the communication element24. For example, the processing element 28 may communicate with theseand possibly other electronic components through serial or parallellinks that include address busses, data busses, control lines, and thelike.

The system 20 may include more, fewer, or alternative components and/orperform more, fewer, or alternative actions, including those discussedelsewhere herein, and particularly those discussed in the followingsection describing the computer-implemented method.

Exemplary Computer-Implemented Method

Referring to FIG. 3 , an exemplary computer-implemented method 100 isshown for evaluating an insurance applicant as part of an underwritingprocess to determine one or more appropriate terms of life or otherinsurance coverage, which may include appropriate premiums or discounts,such as discounts for risk averse individuals or those living a healthylifestyle. Broadly, the computer-implemented method may comprise thefollowing actions. The processing element 28 may be trained toprobablistically correlate a one or more aspects of appearance and/orvoice with a personal or health-related characteristic by providing theprocessing element 28 with the database stored on the memory element 22of still and/or moving (e.g., video) images and/or voice recordings ofindividuals having known personal or health-related characteristics, asshown in 102. The communication element 24 may receive the still and/ormoving image and/or voice recording of the insurance applicant, as shownin 104. The still and/or moving (e.g., video) image and/or voicerecording received from the applicant may be analog or digital andotherwise non-diagnostic and conventional in nature, such as an ordinaryselfie taken by the insurance applicant or him- or herself. The trainedprocessing element 28 may analyze the image of the insurance applicantto probablistically determine the personal or health-relatedcharacteristic for the insurance applicant, as shown in 106. Theprocessing element 28 may suggest the appropriate term of insurancecoverage, such as an appropriate premium, based at least in part on theprobablistically determined personal or health-related characteristic,as shown in 108. The processing element 28 may be trained usingsupervised or unsupervised machine learning. Further, the processingelement 28 may employ a neural network 34, which may be a CNN or a deeplearning neural network.

The computer-implemented method may include more, fewer, or alternativeactions, including those discussed elsewhere herein.

Exemplary Computer-Readable Medium

Referring again to FIGS. 2 and 3 , an exemplary non-transitorycomputer-readable medium with one or more executable programs storedthereon may be configured for evaluating an insurance applicant as partof an underwriting process to determine one or more appropriate terms oflife or other insurance coverage, which may include appropriatepremiums. Broadly, the one or more programs may instruct thecommunication element 24, processing element 28, and/or other componentsof the system 20 to perform the following actions. The processingelement 28 may be trained to probablistically correlate a one or moreaspects of appearance and/or voice with a personal or health-relatedcharacteristic by providing the processing element 28 with the databasestored on the memory element 22 of still and/or moving (e.g., video)images, and/or voice recordings of individuals having known personal orhealth-related characteristics, as shown in 102. The communicationelement 24 may be instructed to receive the still and/or moving image,and/or voice recording of the insurance applicant, as shown in 104. Thestill and/or moving image, and/or voice recording received from theapplicant may be analog or digital and otherwise non-diagnostic andconventional in nature, such as an ordinary selfie taken by theinsurance applicant or him- or herself. The trained processing element28 may be instructed to analyze the image of the insurance applicant toprobablistically determine the personal or health-related characteristicfor the insurance applicant, as shown in 106. The processing element 28may be instructed to suggest the appropriate term of insurance coverage,such as an appropriate premium, based at least in part on theprobablistically determined personal and/or health-relatedcharacteristic, as shown in 108. The processing element 28 may betrained using supervised or unsupervised machine learning. Further, theprocessing element 28 may employ a neural network 34, which may be a CNNor a deep learning neural network.

The one or more executable programs stored on the non-transitorycomputer-readable medium may instruct the system 20 to perform more,fewer, or alternative actions, including those discussed elsewhereherein, and particularly those discussed in the section describing thecomputer-implemented method.

Exemplary Video Functionality

In one aspect, a video magnification system may use a short video of anapplicant and extract the necessary health and personal data without theneed for fluid samples or medical review. For example, the video may beused to calculate the applicant's pulse, and could evolve to detectmedications or drug use through eye movements, and lead to otherinformation such as glucose levels and other measurements normallyattained through bodily fluid analysis. The results may be used toeither answer underwriting questions or automate the underwritingprocess by predicting the appropriate premium directly. Also, the use ofvideo magnification data may help prevent fraud by removing applicants'ability to enter fraudulent information and ensuring an applicant'sidentity.

In one embodiment, an online life or health insurance applicant may befilling out a virtual insurance application online, such as via theirmobile device or another computing device. The virtual insuranceapplication may ask the applicant to submit a short video or images ofthemselves, such as taken via their mobile device, for use withgenerating or adjusting an insurance quote, policy, premium, or discount(such as a life or health insurance application). The applicant maytransmit the short video or images of themselves from their mobiledevice to an insurance provider remote server, or otherwiseelectronically attach the short video or images to the virtual insuranceapplication. Then with the customer's permission or affirmative consent,the insurance provider remote server or another processor may analyzethe short video or images, such as via video magnification or otherdigital image techniques, to identify risk, or lack thereof, associatedwith the applicant, or otherwise determine certain healthcharacteristics of the applicant. For instance, pulse, heart rate,medication or drug use, cigarette or alcohol use, glucose levels,cholesterol level, age, weight, an amount of exercise, sex, etc. may bedetermined from video or image analysis (such as be noticing pulsemovement or eye movement). As an example, cigarette use may bedetermined from image analysis of a person's teeth or gums, cholesterollevel may be determined from image analysis of a person's eyes, pulse orheart rate may be determined from image analysis of a person's neck orveins.

Based upon the risk, or lack thereof identified, or healthcharacteristics determined (such as pulse or glucose levels), theinsurance provider may estimate an insurance premium or discount for theapplicant, and transmit the insurance premium or discount to theapplicant's mobile device, via wireless communication or datatransmission, for the applicant's review, approval, or modification. Asa result, an online customer shopping experience for life or healthinsurance may be enhanced, and the need for invasive procedures, such asgiving blood, may be reduced.

Exemplary Audio Functionality

In another aspect, audio analysis techniques may use an audio recordingof an applicant's voice and extract the necessary health and personaldata without the need for fluid samples or medical review. The voiceanalysis system may learn to identify patterns and characteristics invoice recordings that are indicative of the presence of certain diseasesor medications, or be able to detect other characteristics of anapplicant, such as tobacco use. The results may be used to either answerunderwriting questions or automate the underwriting process bypredicting the appropriate premium directly. Further, the use of theaudio analysis system may help prevent fraud by removing applicants'ability to enter fraudulent information and ensuring an applicant'sidentity.

In one embodiment, an online life or health insurance applicant may befilling out a virtual insurance application online, such as via theirmobile device or another computing device. The virtual insuranceapplication may ask the applicant to submit a short audio of themselves,such as recorded via their mobile device (e.g., voice recorded within avideo), for use with generating or adjusting an insurance quote, policy,premium, or discount (such as a life or health insurance application).The applicant may transmit the short audio recording of themselves fromtheir mobile device to an insurance provider remote server, or otherwiseelectronically attach the short audio recording to the virtual insuranceapplication. Then with the customer's permission or affirmative consent,the insurance provider remote server or another processor may analyzethe short audio recording, such as via audio analysis or other digitalaudio processing techniques, to identify risk, or lack thereof,associated with the applicant, or otherwise determine certain healthcharacteristics of the applicant. For instance, certain diseases ormedication use, as well as cigarette use, age, weight, sex may bedetermined or estimated from audio analysis. Based upon the risk, orlack thereof identified, or health characteristics determined (such aslack of smoking), the insurance provider may estimate an insurancepremium or discount for the applicant, and transmit the insurancepremium or discount to the applicant's mobile device, via wirelesscommunication or data transmission, for the applicant's review,approval, or modification.

Exemplary Computer-Implemented Methods

FIG. 4 depicts an exemplary computer-implemented method 400 of providinglife or health insurance quotes based upon, at least in part, video,image, or audio data samples received via wireless communication or datatransmission from an applicant's mobile device. The method 400 mayinclude with an individuals' permission, gathering video, images, oraudio of those with known health conditions or risk levels, or lackthereof 402. For instance, images, images, and/or audio of those withcertain diseases, associated with certain medication or drug use,associated with cigarette or alcohol usage, of a certain age or weight,of a specific cholesterol or glucose level, or having other specifichealth conditions or risk levels, or a lack of a health condition orailment, or having a low risk level.

The method 400 may include analyzing the collected video, image(s), oraudio to build a database, table or other data structure correspondingto the known health conditions 404. For instance, a two column datastructure could include exemplary video, image(s), or audio (stored orlinked to by a pointer in column 1) that is associated with a certainhealth condition or risk level (that is stored in column 2 of the datastructure or table). Additionally or alternatively, instead of buildinga database, a neural network may be trained to identify certain healthconditions or risk levels from processor analysis of video, images, oraudio of individuals having known health conditions or ailments, such asdescribed elsewhere herein.

The method 400 may include asking an insurance applicant to submit avideo, image, or audio sample via their mobile device for a virtual lifeor health insurance application 406. For instance, after a database orneural network is trained to identify health conditions or health riskfrom video, image, or audio analysis, an online or virtual insuranceapplication form may include functionality that allows an application toattach a video, image, or audio sample to a virtual application usingtheir mobile device. The virtual application may give an applicant theoption submitting video, image, or audio data of themselves, and ask forconsent for an insurance provider to analyze the data sample submitted.In return, the applicant may not be required to submit to invasiveprocedures, such as drawing blood (or even a visit to nurse or doctor),and risk averse applicants may be entitled to a discount, such as thosethat don't smoke or drink heavily, or that exercise or otherwise live ahealth conscious life.

The method 400 may include analyzing the video, image, or audio datasample received from the application with the applicant's permission oraffirmative consent 408. For instance, the applicant's mobile device maytransmit the video, image, or audio data sample to an insurance providerremote server, and the data sample may be analyzed via comparison ofvideo, image, or audio data stored in the database of known healthconditions, or otherwise used to train a neural network.

The method 400 may include, from comparison of the online applicants'video, image, or audio data sample with video, image, audio data storedin the database (or used to train the neural network) to determinehealth conditions or risk level for the applicant 410. From analysis ofthe data sample various health conditions may be determined via aprocessor, such sex, weight, body mass index, cholesterol, amount ofexercise, cigarette or alcohol use, medication or drug use, certaindiseases or ailments, glucose levels, and other health conditions orrisks, including those discussed elsewhere herein.

The method 400 may include generating or adjusting (such as at aninsurance provider remote server) an insurance policy, premium, ordiscount based upon the health conditions or risk levels determined 412.For instance, risk averse applicants may have their lack of riskverified via their video, image, or data samples, such as lack ofsmoking or drug use, or an appropriate amount of exercise. As a result,those applicants may be receive an online insurance discount on healthor life insurance, for instance.

The method 400 may include transmitting an insurance quote, premium, ordiscount from an insurance provider remote server to the applicant'smobile device for their review and approval 414. For instance, a quotefor insurance may be transmitted to an applicant's mobile or othercomputing device for their review and approval via wirelesscommunication and/or data transmission to enhance on online customerexperience associated with shopping for and/or receiving binding life orhealth insurance.

In another aspect, a computer-implemented method for evaluating aninsurance applicant as part of an underwriting process to determine alife or health insurance policy, premium, or discount may be provided.The computer-implemented method may include (1) training a processingelement to probablistically correlate an aspect of appearance with apersonal and/or health-related characteristic by providing theprocessing element with a database of images of individuals having knownpersonal or health-related characteristics; (2) receiving with acommunication element an image of the insurance applicant; (3) analyzingthe image of the insurance applicant with the trained processing elementto probablistically determine the personal and/or health-relatedcharacteristic for the insurance applicant; and/or (4) generating oradjusting via the processing element a life or health insurance policy,premium, or discount based at least in part on the probablisticallydetermined personal and/or health-related characteristic.

The personal and/or health-related characteristic may be a pulse orheart rate. The personal and/or health-related characteristic mayindicate, or be associated with, smoking, a lack of smoking, or anamount or frequency of smoking. The personal and/or health-relatedcharacteristic may indicate, or be associated with, drug or alcohol use,a lack of drug or alcohol use, or an amount or frequency of drug oralcohol use.

In another aspect, a computer-implemented method for evaluating aninsurance applicant as part of an underwriting process to determine anappropriate life insurance premium may be provided. Thecomputer-implemented method may include (1) training a processingelement having a neural network to probablistically correlate one ormore aspects of appearance with a personal and/or health-relatedcharacteristic by providing the processing element with a database ofotherwise non-diagnostic conventional images of individuals having knownpersonal and/or health-related characteristics; (2) receiving with acommunication element an otherwise non-diagnostic conventional image ofthe insurance applicant; (3) analyzing with the trained processingelement the otherwise non-diagnostic conventional image of the insuranceapplicant to probablistically determine the personal and/orhealth-related characteristic for the insurance applicant; (4) using, bythe processing element, the probablistically determined personal and/orhealth-related characteristic to verify information provided by theinsurance applicant; and/or (5) automatically determining or adjustingwith the processing element a life or health insurance premium ordiscount based at least in part on the probablistically determinedpersonal and/or health-related characteristic.

In another aspect, a computer-implemented method for evaluatingapplicant provided images to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors, image data from an applicant's mobile device; (2) comparing,via the one or more processors, the image data with images stored in adatabase that have corresponding pre-determined health conditions orrisk levels; (3) identifying, via the one or more processors, a healthcondition or risk level for the applicant based upon the comparison ofthe applicant's image data with the images stored in the database;and/or (4) automatically determining or adjusting, via the one or moreprocessors, a life or health insurance premium or discount based atleast in part on the health condition or risk level for the applicantthat is determined from their image data to facilitate providing moreaccurate or appropriate insurance premiums in view of risk, or lackthereof, to insurance customers. The health condition or risk level maybe associated with or determined based upon whether or not the applicantsmokes, or an amount that the applicant smokes (determined fromprocessor analysis of the applicant's image data). The health conditionor risk level may be associated with, or determined based upon, whetheror not the applicant uses drugs, or pulse or heart rate of the applicantdetermined from processor analysis of the applicant's image data.

In another aspect, a computer-implemented method for evaluatingapplicant provided images to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors (and/or associated transceivers, such as via wirelesscommunication or data transmission), an indication from an applicant'smobile device that the applicant is interested in applying for insuranceand/or receiving an online insurance application from the applicant'smobile device; (2) transmitting, via the one or more processors (and/orassociated transceivers), a request for the applicant to transmit imagedata of the applicant from their mobile device for use with determiningan accurate insurance premium or discount; (3) receiving, via one ormore processors (and/or associated transceivers), the image data fromthe applicant's mobile device; (4) identifying, via the one or moreprocessors, a health condition or risk level for the applicant (and/or apersonal and/or health-related characteristic) based upon the computeranalysis of the applicant's image data; (5) automatically determining oradjusting, via the one or more processors, a life or health insurancepremium or discount based at least in part on the health condition orrisk level for the applicant that is determined from their image data;and/or (6) transmitting, via the one or more processors (and/or anassociated transceiver), the life or health insurance premium ordiscount to the applicant's mobile device for their review and/orapproval to facilitate providing more accurate insurance premiums toinsurance customers and enhancing the online customer experience. Thehealth condition or risk level (and/or personal and/or health-relatedcharacteristic) determined from computer analysis of the applicant'simage data may be a pulse or heart rate. The health condition or risklevel (and/or personal and/or health-related characteristic) determinedfrom computer analysis of the applicant's image data may indicate, ormay be associated with, smoking, a lack of smoking, or an amount orfrequency of smoking. Additionally or alternatively, the healthcondition or risk level (and/or personal and/or health-relatedcharacteristic) determined from computer analysis of the applicant'simage data may indicate, or be associated with, drug or alcohol use, alack of drug or alcohol use, or an amount or frequency of drug oralcohol use.

In another aspect, a computer-implemented method for evaluatingapplicant provided audio to adjust or generate a life or healthinsurance policy, premium, or discount may be provided. Thecomputer-implemented method may include (1) receiving, via one or moreprocessors, an indication from an applicant's mobile device that theapplicant is interested in applying for insurance and/or receiving anonline insurance application from the applicant's mobile device; (2)transmitting, via the one or more processors, a request for theapplicant to transmit audio data of the applicant from their mobiledevice for use with determining an accurate insurance premium ordiscount; (3) receiving, via one or more processors, the audio data fromthe applicant's mobile device; (4) identifying, via the one or moreprocessors, a health condition or risk level for the applicant (and/or apersonal and/or health-related characteristic) based upon the computeranalysis of the applicant's audio data; (5) automatically determining oradjusting, via the one or more processors, a life or health insurancepremium or discount based at least in part on the health condition orrisk level for the applicant that is determined from their audio data;and/or (6) transmitting, via the one or more processors, the life orhealth insurance premium or discount to the applicant's mobile devicefor their review and/or approval to facilitate providing more accurateinsurance premiums to insurance customers and enhancing the onlinecustomer experience.

The foregoing methods may include additional, less, or alternatefunctionality, including that discussed elsewhere herein. Further, theforegoing methods may be implemented via one or more local or remoteprocessors and/or transceivers, and/or via computer-executableinstructions stored on non-transitory computer-readable medium or media.

Exemplary Computer Systems

In one aspect, a computer system for evaluating an insurance applicantas part of an underwriting process to determine one or more appropriateterms of insurance coverage may be provided. The system may include acommunication element configured to receive an image of the insuranceapplicant; and a processing element—trained to probablisticallycorrelate an aspect of appearance with a personal and/or health-relatedcharacteristic by being provided with a database of images ofindividuals having known personal and/or health-related characteristics,and configured to analyze the image of the insurance applicant toprobablistically determine the personal and/or health-relatedcharacteristic for the insurance applicant, and to generate a proposedlife or health insurance policy, premium, or discount for the insuranceapplicant based upon the personal and/or health-related characteristicprobablistically determined from the image.

The communication element may be further configured to receive an voicerecording of the insurance applicant; and the processing elementis—trained to probablistically correlate an aspect of voice with thepersonal and/or health-related characteristic by being provided with adatabase of voice recordings of individuals having the known personaland/or health related characteristics, and configured to analyze thevoice recording of the insurance applicant to probablistically determinethe personal and/or health-related characteristic for the insuranceapplicant, and generate or adjust a proposed life or health insurancepolicy, premium, or discount based upon at least in part theprobablistically determined personal and/or health-relatedcharacteristic.

The personal and/or health-related characteristic may be a pulse orheart rate. The personal and/or health-related characteristic mayindicate, or be associated with, smoking, a lack of smoking, or anamount or frequency of smoking. The personal and/or health-relatedcharacteristic may indicate, or be associated with, alcohol or drug use,a lack of alcohol or drug use, or an amount or frequency of alcohol ordrug use.

In another aspect, a computer system for evaluating an insuranceapplicant as part of an underwriting process to determine a life orhealth insurance policy, premium, or discount may be provided. Thecomputer system may include one or more processors configured to: (1)train a processing element to probablistically correlate an aspect ofappearance with a personal and/or health-related characteristic byproviding the processing element with a database of images ofindividuals having known personal or health-related characteristics; (2)receive with a communication element an image of the insuranceapplicant; (3) analyze the image of the insurance applicant with thetrained processing element to probablistically determine the personaland/or health-related characteristic for the insurance applicant; and/or(4) generate or adjust via the processing element a life or healthinsurance policy, premium, or discount based at least in part on theprobablistically determined personal and/or health-relatedcharacteristic.

In another aspect, a computer system for evaluating an insuranceapplicant as part of an underwriting process to determine an appropriatelife insurance premium may be provided. The computer system may includeone or more processors configured to: (1) train a processing elementhaving a neural network to probablistically correlate one or moreaspects of appearance with a personal and/or health-relatedcharacteristic by providing the processing element with a database ofotherwise non-diagnostic conventional images of individuals having knownpersonal and/or health-related characteristics; (2) receive with acommunication element an otherwise non-diagnostic conventional image ofthe insurance applicant: (3) analyze with the trained processing elementthe otherwise non-diagnostic conventional image of the insuranceapplicant to probablistically determine the personal and/orhealth-related characteristic for the insurance applicant; (4) use, byor via the processing element, the probablistically determined personaland/or health-related characteristic to verify information provided bythe insurance applicant; and/or (5) automatically determine or adjustwith the processing element a life or health insurance premium ordiscount based at least in part on the probablistically determinedpersonal and/or health-related characteristic.

In another aspect, a computer system for evaluating applicant providedimages to adjust or generate a life or health insurance policy, premium,or discount may be provided. The computer system may include one or moreprocessors and/or associated transceivers configured to: (1) receiveimage data from an applicant's mobile device, such as via wirelesscommunication or data transmission; (2) compare the image data withimages stored in a database that have corresponding pre-determinedhealth conditions or risk levels; (3) identify a health condition orrisk level (such as low, medium, or high risk) for the applicant basedupon the comparison of the applicant's image data with the images storedin the database; and/or (4) automatically determine or adjust a life orhealth insurance premium or discount based at least in part on thehealth condition or risk level for the applicant that is determined fromtheir image data to facilitate providing more accurate or appropriateinsurance premiums in view of risk, or lack thereof, to insurancecustomers. The health condition or risk level may be associated with ordetermined based upon whether or not the applicant smokes, or an amountthat the applicant smokes (determined from processor analysis of theapplicant's image data). The health condition or risk level may beassociated with, or determined based upon, whether or not the applicantuses drugs, or pulse or heart rate of the applicant determined fromprocessor analysis of the applicant's image data.

In another aspect, a computer system for evaluating applicant providedimages to adjust or generate a life or health insurance policy, premium,or discount may be provided. The computer system may include one or moreprocessors and/or transceivers configured to: (1) receive an indication,via wireless communication or data transmission, from an applicant'smobile device that the applicant is interested in applying for insuranceand/or receive an online or virtual insurance application from theapplicant's mobile device; (2) transmit, via wireless communication ordata transmission, a request for the applicant to transmit image data ofthe applicant from their mobile device for use with determining anaccurate insurance premium or discount; (3) receive, via wirelesscommunication or data transmission, the image data from the applicant'smobile device; (4) identify or determine a health condition or risklevel for the applicant (and/or a personal and/or health-relatedcharacteristic) based upon the computer analysis of the applicant'simage data (with the customer's permission or affirmative consent); (5)automatically determine or adjust a life or health insurance premium ordiscount based at least in part on the health condition or risk levelfor the applicant that is determined from their image data; and/or (6)transmit an estimated insurance premium or discount to the applicant'smobile device for their review and approval to facilitate providing moreaccurate insurance premiums to insurance customers and enhancing theonline customer experience.

In another aspect, a computer system configured for evaluating applicantprovided audio to adjust or generate a life or health insurance policy,premium, or discount may be provided. The computer system may includeone or more processors and/or transceivers configured to: (1) receive anindication from an applicant's mobile device that the applicant isinterested in applying for insurance and/or receiving an onlineinsurance application from the applicant's mobile device; (2) transmit arequest for the applicant to transmit audio data of the applicant fromtheir mobile device for use with determining an accurate insurancepremium or discount; (3) receive the audio data from the applicant'smobile device; (4) identify or determine a health condition or risklevel for the applicant (and/or a personal and/or health-relatedcharacteristic) based upon the computer analysis of the applicant'saudio data (with the customer's permission or affirmative consent); (5)automatically determine or adjust a life or health insurance premium ordiscount based at least in part on the health condition or risk levelfor the applicant that is determined from their audio data; and/or (6)transmit the life or health insurance premium or discount to theapplicant's mobile device for their review and/or approval to facilitateproviding more accurate insurance premiums to insurance customers andenhancing the online customer experience.

The foregoing computer systems may be configured with additional, less,or alternate functionality, including that discussed elsewhere hereinand that described with respect to FIG. 4 . The foregoing computersystems may include computer-executable instructions stored onnon-transitory computer-readable medium or media.

ADDITIONAL CONSIDERATIONS

In this description, references to “one embodiment”, “an embodiment”, or“embodiments” mean that the feature or features being referred to areincluded in at least one embodiment of the technology. Separatereferences to “one embodiment”, “an embodiment”, or “embodiments” inthis description do not necessarily refer to the same embodiment and arealso not mutually exclusive unless so stated and/or except as will bereadily apparent to those skilled in the art from the description. Forexample, a feature, structure, act, etc. described in one embodiment mayalso be included in other embodiments, but is not necessarily included.Thus, the current technology may include a variety of combinationsand/or integrations of the embodiments described herein.

Although the present application sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof routines, subroutines, applications, or instructions. These mayconstitute either software (e.g., code embodied on a machine-readablemedium or in a transmission signal) or hardware. In hardware, theroutines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) ascomputer hardware that operates to perform certain operations asdescribed herein.

In various embodiments, computer hardware, such as a processing element,may be implemented as special purpose or as general purpose. Forexample, the processing element may comprise dedicated circuitry orlogic that is permanently configured, such as an application-specificintegrated circuit (ASIC), or indefinitely configured, such as an FPGA,to perform certain operations. The processing element may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement the processingelement as special purpose, in dedicated and permanently configuredcircuitry, or as general purpose (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “processing element” or equivalents should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which the processing element is temporarily configured(e.g., programmed), each of the processing elements need not beconfigured or instantiated at any one instance in time. For example,where the processing element comprises a general-purpose processorconfigured using software, the general-purpose processor may beconfigured as respective different processing elements at differenttimes. Software may accordingly configure the processing element toconstitute a particular hardware configuration at one instance of timeand to constitute a different hardware configuration at a differentinstance of time.

Computer hardware components, such as communication elements, memoryelements, processing elements, and the like, may provide information to,and receive information from, other computer hardware components.Accordingly, the described computer hardware components may be regardedas being communicatively coupled. Where multiple of such computerhardware components exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the computer hardware components. In embodimentsin which multiple computer hardware components are configured orinstantiated at different times, communications between such computerhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplecomputer hardware components have access. For example, one computerhardware component may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther computer hardware component may then, at a later time, accessthe memory device to retrieve and process the stored output. Computerhardware components may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processing elements thatare temporarily configured (e.g., by software) or permanently configuredto perform the relevant operations. Whether temporarily or permanentlyconfigured, such processing elements may constitute processingelement-implemented modules that operate to perform one or moreoperations or functions. The modules referred to herein may, in someexample embodiments, comprise processing element-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processing element-implemented. For example, at least some ofthe operations of a method may be performed by one or more processingelements or processing element-implemented hardware modules. Theperformance of certain of the operations may be distributed among theone or more processing elements, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processing elements may be located in a single location(e.g., within a home environment, an office environment or as a serverfarm), while in other embodiments the processing elements may bedistributed across a number of locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer with a processing element andother computer hardware components) that manipulates or transforms datarepresented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus.

Although the invention has been described with reference to theembodiments illustrated in the attached drawing figures, it is notedthat equivalents may be employed and substitutions made herein withoutdeparting from the scope of the invention as recited in the claims.

Having thus described various embodiments of the invention, what isclaimed as new and desired to be protected by Letters Patent includesthe following: 1-20. (canceled)
 21. A computer system comprising: atleast one memory configured to store computer-executable instructions;and at least one processor configured to execute the storedinstructions, which when executed cause the at least one processor toperform operations comprising: receiving, via a communication element ofthe computer system, image data and audio data representing anappearance and voice of an applicant; analyzing the image data and theaudio data of the applicant using a machine-learning (ML) algorithm, theML algorithm configured to determine health-related characteristics forthe applicant based upon an input of the image data and the audio data;and generating or updating a health-related insurance policy based atleast in part on the determined health-related characteristics of theapplicant.
 22. The computer system of claim 21, wherein the ML algorithmis trained to correlate aspects of the appearance and the voice withhealth-related characteristics using a database of image, video, and/oraudio information of individuals having known health-relatedcharacteristics.
 23. The computer system of claim 21, wherein theoperations further comprise determining a life or health insurancepolicy, premium, or discount for the applicant based at least in part onthe verified health-related characteristics of the applicant.
 24. Thecomputer system of claim 21, wherein the health-related characteristicsinclude a pulse or heart rate.
 25. The computer system of claim 21,wherein the health-related characteristics indicate, or are at leastpartly associated with, smoking, a lack of smoking, or an amount orfrequency of smoking.
 26. The computer system of claim 21, wherein thehealth-related characteristics indicate, or are at least partlyassociated with, drug or alcohol use, a lack of drug or alcohol use, oran amount or frequency of drug or alcohol use.
 27. The computer systemof claim 21, wherein the ML algorithm is trained using supervisedmachine learning.
 28. The computer system of claim 21, wherein the MLalgorithm is implemented using a neural network.
 29. The computer systemof claim 28, wherein the neural network is a convolutional neuralnetwork.
 30. The computer system of claim 28, wherein the neural networkis a deep learning neural network.
 31. The computer system of claim 21,wherein the health-related characteristics are selected from a groupincluding one or more of: age, sex, weight, height, lifespan, cause ofdeath, tobacco use, alcohol use, drug use, diet, and existing medicalconditions.
 32. The computer system of claim 21, wherein the operationsfurther comprise using the determined health-related characteristics ofthe applicant to substantially and automatically determine appropriateterms of coverage for a life or health insurance policy.
 33. Acomputer-implemented method, comprising: receiving image data and audiodata representing an appearance and voice of an applicant; analyzing theimage data and the audio data of the applicant using a machine-learning(ML) algorithm, the ML algorithm configured to determine health-relatedcharacteristics for the applicant based upon an input of the image dataand the audio data; and generating or updating a health-relatedinsurance policy based at least in part on the determined health-relatedcharacteristics of the applicant.
 34. The computer-implemented method ofclaim 33, further comprising training the ML algorithm to correlateaspects of the appearance and the voice with health-relatedcharacteristics using a database of image, video, and/or audioinformation of individuals having known health-related characteristics.35. The computer-implemented method of claim 33, further comprisingdetermining a life or health insurance policy, premium, or discount forthe applicant based at least in part on the verified health-relatedcharacteristics of the applicant, wherein the health-relatedcharacteristics include, indicate, or are at least partly associatedwith, smoking, a lack of smoking, or an amount or frequency of smoking,a pulse rate, heart rate, drug or alcohol use, a lack of drug or alcoholuse, or an amount or frequency of drug or alcohol use.
 36. Thecomputer-implemented method of claim 33, wherein the health-relatedcharacteristics are selected from a group including one or more of: age,sex, weight, height, lifespan, cause of death, tobacco use, alcohol use,drug use, diet, and existing medical conditions.
 37. Thecomputer-implemented method of claim 33, further comprise using thedetermined health-related characteristics of the applicant tosubstantially and automatically determine appropriate terms of coveragefor a life or health insurance policy.
 38. At least one non-transitorycomputer-readable media having computer-executable instructions embodiedthereon, wherein when executed by a computing device including at leastone processor in communication with at least one memory device and incommunication with a mobile device of an applicant, thecomputer-executable instructions cause the at least one processor to:receive, from the mobile device of the applicant, image data and audiodata representing an appearance and voice of the applicant; analyze theimage data and audio date of the applicant using a machine-learning (ML)algorithm, the ML algorithm configured to determine health-relatedcharacteristics for the applicant based upon an input of the image dataand the audio data; and generate or update a health-related insurancepolicy based at least in part on the determined health-relatedcharacteristics of the applicant.
 39. The least one non-transitorycomputer-readable media of claim 38, wherein the computer-executableinstructions further cause the at least one processor to train the MLalgorithm to correlate aspects of the appearance and the voice withhealth-related characteristics using a database of image, video, and/oraudio information of individuals having known health-relatedcharacteristics.
 40. The least one non-transitory computer-readablemedia of claim 38, wherein the computer-executable instructions furthercause the at least one processor to use the determined health-relatedcharacteristics of the applicant to substantially and automaticallydetermine appropriate terms of coverage for a life or health insurancepolicy.